{"type":"ScholarlyArticle","title":"Multi-View Active Learning for Video Recommendation","authors":[{"name":"Jia-Jia Cai","slug":"jia-jia-cai","given":"Jia-Jia","family":"Cai","affiliation":"College of Computer Science and Technology , Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization , Nanjing , China"},{"name":"Jun Tang","slug":"jun-tang","given":"Jun","family":"Tang","affiliation":"YouKu Cognitive and Intelligent Lab , Alibaba Group , Hangzhou , China"},{"name":"Qing-Guo Chen","slug":"qing-guo-chen","given":"Qing-Guo","family":"Chen","affiliation":"YouKu Cognitive and Intelligent Lab , Alibaba Group , Hangzhou , China"},{"name":"Yao Hu","slug":"yao-hu","given":"Yao","family":"Hu","affiliation":"YouKu Cognitive and Intelligent Lab , Alibaba Group , Hangzhou , China"},{"name":"Xiaobo Wang","slug":"xiaobo-wang","given":"Xiaobo","family":"Wang","affiliation":"YouKu Cognitive and Intelligent Lab , Alibaba Group , Hangzhou , China"},{"name":"Sheng-Jun Huang","slug":"sheng-jun-huang","given":"Sheng-Jun","family":"Huang","affiliation":"College of Computer Science and Technology , Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization , Nanjing , China"}],"abstract":"On many video websites, the recommendation is implemented as a prediction problem of video-user pairs, where the videos are represented by text features extracted from the metadata. However, the metadata is manually annotated by users and is usually missing for online videos. To train an effective recommender system with lower annotation cost, we propose an active learning approach to fully exploit the visual view of videos, while querying as few annotations as possible from the text view. On one hand, a joint model is proposed to learn the mapping from visual view to text view by simultaneously aligning the two views and minimizing the classification loss. On the other hand, a novel strategy based on prediction inconsistency and watching frequency is proposed to actively select the most important videos for metadata querying. Experiments on both classification datasets and real video recommendation tasks validate that the proposed approach can significantly reduce the annotation cost.","doi":"10.24963/ijcai.2019/284","url":"https://ijcai.org/proceedings/2019/284","pdf":"https://ijcai.org/proceedings/2019/0284.pdf","markdown":"https://ijcai.org/proceedings/2019/284.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/284","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":284,"pages":{"first":2053,"last":2059},"keywords":["Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning: Classification","Machine Learning: Semi-Supervised Learning","Machine Learning: Active Learning"],"videos":[],"references":[{"position":1,"raw":"[Abu-El-Haija et al., 2016] Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijaya- narasimhan. 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